Diffusion Reward: Learning Rewards via Conditional Video Diffusion
Abstract
Learning rewards from expert videos offers an affordable and effective solution to specify the intended behaviors for reinforcement learning (RL) tasks. In this work, we propose , a novel framework that learns rewards from expert videos via conditional video diffusion models for solving complex visual RL problems. Our key insight is that lower generative diversity is exhibited when conditioning diffusion on expert trajectories. is accordingly formalized by the negative of conditional entropy that encourages productive exploration of expert behaviors. We show the efficacy of our method over robotic manipulation tasks in both simulation platforms and the real world with visual input. Moreover, can even solve unseen tasks successfully and effectively, largely surpassing baseline methods. Project page and code: citecolordiffusion-reward.github.io.
Cite
Text
Huang et al. "Diffusion Reward: Learning Rewards via Conditional Video Diffusion." Proceedings of the European Conference on Computer Vision (ECCV), 2024. doi:10.1007/978-3-031-72946-1_27Markdown
[Huang et al. "Diffusion Reward: Learning Rewards via Conditional Video Diffusion." Proceedings of the European Conference on Computer Vision (ECCV), 2024.](https://mlanthology.org/eccv/2024/huang2024eccv-diffusion/) doi:10.1007/978-3-031-72946-1_27BibTeX
@inproceedings{huang2024eccv-diffusion,
title = {{Diffusion Reward: Learning Rewards via Conditional Video Diffusion}},
author = {Huang, Tao and Jiang, Guangqi and Ze, Yanjie and Xu, Huazhe},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2024},
doi = {10.1007/978-3-031-72946-1_27},
url = {https://mlanthology.org/eccv/2024/huang2024eccv-diffusion/}
}